Justin Gottschlich

Lead Artificial Intelligence Researcher, Programming Systems Research, Intel Labs

Steering Committee Chair, Machine Learning and Programming Languages Workshop

Chair, Industrial Board for PRECISE Center, University of Pennsylvania

Executive Director of AI Research and Development, PRECISE Center for Safe AI, University of Pennsylvania

Principal Investigator and Co-Founder, Intel/NSF CAPA Research Center


Maaz Ahmad (advised by Alvin Cheung @ Berkeley)

Akhilesh Gupta (advised by Insup Lee @ Penn)

Sam Weintraub (advised by Insup Lee @ Penn)

Fangke Ye (advised by Vivek Sarkar @ Georgia Tech)


TheWebConf'20 (Intelligent Systems and Infrastructure Track), MAPL'20 (SC chair), SysML'20, PACT'19 (SRC), SysML'19, MAPL'18 (general chair), MAPL'17 (program chair)

Contact: justin.gottschlich@intel.com


At Intel Labs, I am the lead artificial intelligence researcher for programming systems research and the principal investigator and co-founder of the joint Intel/NSF CAPA research center. In academia, I have appointments as the executive director of AI research and development for the PRECISE Center for Safe AI at the University of Pennsylvania and as the chair of the PRECISE industrial advisory board.

Overall, I perform research in artificial intelligence with a focus on machine programming, anomaly detection, deep learning, and autonomous systems. I try to build and maintain deep academic and industrial ties. I'm wildly interested in machine programming. In 2016, I co-founded the machine learning and programming languages (MAPL) workshop and was its program and general chair in 2017 and 2018, respectively. In 2019, I accepted the invitation as the chair of the MAPL steering committee.

I am an adjunct professor at the University of Colorado-Boulder and a lecturer at the University of Pennsylvania on anomaly detection for safe autonomy. I was previously the director of engineering at Machine Zone, where I oversaw the engineering of Game of War and Mobile Strike. When not doing research, I work on my online gaming software company, Nodeka, LLC, which I founded in 1999.

My (somewhat dated) CV is here. I have over 60 peer reviewed publications and issued patents with around 100 patents pending. I've given several dozen invited research presentations at places like Berkeley, BMW, IBM Research, Penn, Stanford, VMWare, UCLA, and UW.



Patent issued: "Compute optimization mechanism for deep neural networks" (10,417,734)

Patent issued: "Compute optimization mechanism for deep neural networks" (10,417,731)

Patent issued: "Autonomous machines through cloud, error corrections, and predictions"

Accepted to NeurIPS: "A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions"

Opening address for Machine Programming Day @ Berkeley: "Intel's Machine Programming Pioneering Research Vision"

Patent issued: "Mechanism for facilitating dynamic and efficient management of instruction atomicity violations in software programs at computing systems"

Patent issued: "Methods and systems to identify and reproduce concurrency violations in multi-threaded programs using expressions"

Accepted invitation as chair of MAPL steering committee.

Q2 Intel Labs' Eureka Award Winner (inventor with most patent applications filed in a quarter (30 new filings)).

Accepted invitation to serve on SysML 2020 program committee.

Patent issued (milestone, 25th issued patent): "Autonomous vehicle advanced sensing and response"

Intel's Annual Gordon Moore Award Nomination: "Informed risk-taking across Intel Labs, PSG, SSG, and University Research that has furthered Intel's FPGA innovations"

      • Category: Excellence in Risk Taking.
      • Team: Aravind Dasu, Mahesh Iyer, Eriko Nurvitadhi, Michael Adler, Justin Gottschlich, Mondira Pant, Todd Younkin

Intel Tech Insights Leadership Award: "Machine Programming: A Radical Approach to Automating Software" (Justin Gottschlich and Tim Mattson)

Patent issued: "Coordination and increased utilization of graphics processors during inference"

Patent issued: "Extend GPU/CPU coherency to multi-GPU cores"

DATSA has been open sourced.

SysML whitepaper: "SysML: The New Frontier of Machine Learning Systems"

Invited talk, Stanford DAWN Retreat '19: "Machine Programming"

Patent issued: "Detecting root causes of use-after-free memory errors"

Patent issued: "Methods and systems to identify and reproduce concurrency violations in multi-threaded programs"

Invited talk to Dawn Song's research team at Berkeley: "Anomaly detection, machine programming, and other AI research at Intel"

Our "Precision and Recall for Time Series" NeurIPS paper has made a few different top paper reading lists. Here's one. Here's another.

Patent issued: "Programmable coarse grained and sparse matrix compute hardware with advanced scheduling."

Co-teaching with Insup Lee and James Weimer: CIS 700-002: Topics in Safe Autonomy, Spring 2019


PhD co-advisor: Irina Calciu, Brown University - VMWare

PhD committee member: Wenjia Ruan, Lehigh University - Qualcomm

PhD committee member: Mohammad Mejbah ul Alam - Intel Labs